The "TechnicalExampleStrategy" is a trading strategy implemented in Python for backtesting purposes. It utilizes various technical indicators to generate buy and sell signals. Here's a breakdown of what the strategy does:
Import necessary libraries and modules.
Define the minimal return on investment (ROI) target as 0.01, indicating a 1% minimum desired return.
Set the stop loss at -0.05, representing a 5% maximum loss tolerance.
Specify the ticker interval as '5m', indicating a 5-minute timeframe for trading. Implement the "populate_indicators" function, which calculates the Chaikin Money Flow (CMF) indicator using a 21-day period and adds it as a new column ('cmf') to the input dataframe. Implement the "populate_buy_trend" function, which identifies buying opportunities based on the condition that the CMF indicator is less than 0. Implement the "populate_sell_trend" function, which identifies selling opportunities based on the condition that the CMF indicator is greater than 0. In summary, this strategy generates buy signals when the CMF indicator is negative (indicating potential oversold conditions), and sell signals when the CMF indicator is positive (indicating potential overbought conditions). The strategy aims for a minimum ROI of 1% and has a stop loss of 5%. The ticker interval for trading is set to 5 minutes.
Define the minimal return on investment (ROI) target as 0.01, indicating a 1% minimum desired return.
Set the stop loss at -0.05, representing a 5% maximum loss tolerance.
Specify the ticker interval as '5m', indicating a 5-minute timeframe for trading. Implement the "populate_indicators" function, which calculates the Chaikin Money Flow (CMF) indicator using a 21-day period and adds it as a new column ('cmf') to the input dataframe. Implement the "populate_buy_trend" function, which identifies buying opportunities based on the condition that the CMF indicator is less than 0. Implement the "populate_sell_trend" function, which identifies selling opportunities based on the condition that the CMF indicator is greater than 0. In summary, this strategy generates buy signals when the CMF indicator is negative (indicating potential oversold conditions), and sell signals when the CMF indicator is positive (indicating potential overbought conditions). The strategy aims for a minimum ROI of 1% and has a stop loss of 5%. The ticker interval for trading is set to 5 minutes.
stoploss: -0.05 timeframe: 5m hash(sha256): a6d8f6f4f5ee2384bfff1b32a37841f374c3181854c6c20c2be650c2f3cc874d indicators: cmf cm
No similar strategies found. (based on used indicators)
last change: 2024-04-29 01:11:47